/* NeuQuant Neural-Net Quantization Algorithm Interface
* ----------------------------------------------------
*
* Copyright (c) 1994 Anthony Dekker
*
* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
* See "Kohonen neural networks for optimal colour quantization"
* in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
* for a discussion of the algorithm.
*
* Any party obtaining a copy of these files from the author, directly or
* indirectly, is granted, free of charge, a full and unrestricted irrevocable,
* world-wide, paid up, royalty-free, nonexclusive right and license to deal
* in this software and documentation files (the "Software"), including without
* limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons who receive
* copies from any such party to do so, with the only requirement being
* that this copyright notice remain intact.
*/
#pragma once
#ifndef _NEUQUANT_H_
#define _NEUQUANT_H_

#include <stdio.h>

#ifndef max
#define max(a,b)    (((a) > (b)) ? (a) : (b))
#endif

#define max_netsize		256			/* number of colours used */

/* For 256 colours, fixed arrays need 8kb, plus space for the image
---------------------------------------------------------------- */


/* four primes near 500 - assume no image has a length so large */
/* that it is divisible by all four primes */
#define prime1		499
#define prime2		491
#define prime3		487
#define prime4		503

#define minpicturebytes	(3*prime4)		/* minimum size for input image */


/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
----------------------------------------------------------------------- */
void initnet(unsigned char *thepic, int len, int sample, int netsize = max_netsize);

/* Unbias network to give byte values 0..255 and record position i to prepare for sort
----------------------------------------------------------------------------------- */
void unbiasnet();	/* can edit this function to do output of colour map */

/* Output colour map
----------------- */
void writecolourmap(void *pal);

/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
------------------------------------------------------------------------------- */
void inxbuild();

/* Search for BGR values 0..255 (after net is unbiased) and return colour index
---------------------------------------------------------------------------- */
int inxsearch(int b, int g, int r);

/* Main Learning Loop
------------------ */
//bool learn(CConvert *convert);
bool learn(void);


void color_error(int r,int g,int b,int p,int *rr,int *gg,int *bb);

/* Program Skeleton
----------------
[select samplefac in range 1..30]
pic = (unsigned char*) malloc(3*width*height);
[read image from input file into pic]
initnet(pic,3*width*height,samplefac);
learn();
unbiasnet();
[write output image header, using writecolourmap(f),
possibly editing the loops in that function]
inxbuild();
[write output image using inxsearch(b,g,r)]		*/

#endif